dc.contributor.author | Varna, Dainius | |
dc.contributor.author | Abromavičius, Vytautas | |
dc.date.accessioned | 2023-09-18T16:19:46Z | |
dc.date.available | 2023-09-18T16:19:46Z | |
dc.date.issued | 2022 | |
dc.identifier.issn | 2076-3417 | |
dc.identifier.other | (crossref_id)137656373 | |
dc.identifier.uri | https://etalpykla.vilniustech.lt/handle/123456789/113236 | |
dc.description.abstract | The presented research addresses the real-time object detection problem with small and moving objects, specifically the surface-mount component on a conveyor. Detecting and counting small moving objects on the assembly line is a challenge. In order to meet the requirements of real-time applications, state-of-the-art electronic component detection and classification algorithms are implemented into powerful hardware systems. This work proposes a low-cost system with an embedded microcomputer to detect surface-mount components on a conveyor belt in real time. The system detects moving, packed, and unpacked surface-mount components. The system’s performance was experimentally investigated by implementing several object-detection algorithms. The system’s performance with different algorithm implementations was compared using mean average precision and inference time. The results of four different surface-mount components showed average precision scores of 97.3% and 97.7% for capacitor and resistor detection. The findings suggest that the system with the implemented YOLOv4-tiny algorithm on the Jetson Nano 4 GB microcomputer achieves a mean average precision score of 88.03% with an inference time of 56.4 ms and 87.98% mean average precision with 11.2 ms inference time on the Tesla P100 16 GB platform. | eng |
dc.format | PDF | |
dc.format.extent | p. 1-17 | |
dc.format.medium | tekstas / txt | |
dc.language.iso | eng | |
dc.relation.isreferencedby | Science Citation Index Expanded (Web of Science) | |
dc.relation.isreferencedby | Scopus | |
dc.relation.isreferencedby | DOAJ | |
dc.relation.isreferencedby | INSPEC | |
dc.relation.isreferencedby | J-Gate | |
dc.source.uri | https://www.mdpi.com/2076-3417/12/11/5608 | |
dc.title | A system for a real-time electronic component detection and classification on a conveyor belt | |
dc.type | Straipsnis Web of Science DB / Article in Web of Science DB | |
dcterms.accessRights | This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/) | |
dcterms.license | Creative Commons – Attribution – 4.0 International | |
dcterms.references | 77 | |
dc.type.pubtype | S1 - Straipsnis Web of Science DB / Web of Science DB article | |
dc.contributor.institution | Vilniaus Gedimino technikos universitetas | |
dc.contributor.faculty | Elektronikos fakultetas / Faculty of Electronics | |
dc.subject.researchfield | T 001 - Elektros ir elektronikos inžinerija / Electrical and electronic engineering | |
dc.subject.researchfield | T 007 - Informatikos inžinerija / Informatics engineering | |
dc.subject.studydirection | E09 - Elektronikos inžinerija / Electronic engineering | |
dc.subject.vgtuprioritizedfields | IK0303 - Dirbtinio intelekto ir sprendimų priėmimo sistemos / Artificial intelligence and decision support systems | |
dc.subject.ltspecializations | L106 - Transportas, logistika ir informacinės ir ryšių technologijos (IRT) / Transport, logistic and information and communication technologies | |
dc.subject.en | convolutional neural networks | |
dc.subject.en | deep learning | |
dc.subject.en | machine learning | |
dc.subject.en | object detection | |
dc.subject.en | SMD component | |
dcterms.sourcetitle | Applied sciences: Special issue: Innovations in intelligent machinery and industry 4.0 | |
dc.description.issue | iss. 12 | |
dc.description.volume | vol. 11 | |
dc.publisher.name | MDPI | |
dc.publisher.city | Basel | |
dc.identifier.doi | 137656373 | |
dc.identifier.doi | 10.3390/app12115608 | |
dc.identifier.elaba | 132099250 | |